gh-francyjglisboa-agent-skill-creator/article-to-prototype-cskill/SKILL.md

Article-to-Prototype Skill

Version: 1.0.0 Type: Simple Skill Architecture: Simple Skill (Single focused objective) Created by: Agent-Skill-Creator v2.1 AgentDB Integration: Enabled

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Table of Contents

1. Overview
2. Core Capabilities
3. Architecture & Design
4. Detailed Component Specifications
5. Extraction Pipeline
6. Analysis Methodology
7. Code Generation Strategy
8. Usage Examples
9. Quality Standards
10. Performance & Optimization
11. AgentDB Integration
12. Error Handling & Recovery
13. Extension Points
14. Testing Strategy
15. Deployment & Installation

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Overview

Purpose

The Article-to-Prototype Skill is an autonomous agent designed to bridge the gap between technical documentation and working code. It extracts technical content from diverse sources (academic papers, blog posts, documentation, tutorials) and generates functional prototypes or proof-of-concept implementations in the most appropriate programming language.

This skill addresses a critical pain point in software development and research: the time-consuming manual translation of algorithms, architectures, and methodologies from written documentation into executable code. By automating this process, developers and researchers can:

• Accelerate prototyping from hours or days to minutes
• Reduce human error in translating complex algorithms
• Maintain traceability between documentation and implementation
• Enable rapid experimentation with new techniques
• Support learning by seeing implementations alongside theory

Problem Statement

Modern software development increasingly relies on implementing techniques and algorithms described in:

• Academic research papers (arXiv, IEEE, ACM)
• Technical blog posts and tutorials
• Official API and library documentation
• Educational materials (books, courses, notebooks)
• Open-source documentation

However, the process of going from "paper to code" involves several manual steps:

1. Reading and comprehending the source material
2. Identifying key algorithms, data structures, and architectures
3. Translating pseudocode or descriptions to actual code
4. Selecting appropriate libraries and frameworks
5. Writing boilerplate and infrastructure code
6. Testing and validating the implementation

This skill automates all these steps while maintaining high quality and accuracy.

Solution Approach

The Article-to-Prototype Skill implements a sophisticated multi-stage pipeline:

1. Format Detection & Extraction: Automatically detects the input format (PDF, web page, notebook, markdown) and applies specialized extraction techniques to preserve structure and content
2. Semantic Analysis: Uses advanced natural language processing to identify technical concepts, algorithms, dependencies, and architectural patterns
3. Language Selection: Intelligently determines the optimal programming language based on the domain, mentioned technologies, and use case
4. Prototype Generation: Generates clean, well-documented, production-quality code with proper error handling and type hints
5. Documentation Creation: Produces comprehensive README files that link back to the source material

Key Differentiators

Unlike generic code generation tools, this skill:

• Preserves context from the original article throughout the implementation
• Handles multiple input formats with specialized extractors for each
• Generates multi-language output based on intelligent analysis
• Includes complete projects with dependencies, tests, and documentation
• Learns progressively through AgentDB integration
• Maintains quality standards with no placeholders or incomplete code

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Core Capabilities

Multi-Format Extraction

PDF Processing

• Academic Papers: Extracts text while preserving section structure, equations (as LaTeX), code blocks, and figure captions
• Technical Reports: Identifies executive summaries, methodologies, and implementation details
• Books & Chapters: Handles multi-column layouts, footnotes, and cross-references
• Presentations: Extracts slide content with logical flow preservation

Techniques Used:

• Layout analysis to detect columns and sections
• Font-based heuristics to identify headings and code
• Table extraction with structure preservation
• Image-to-text extraction for diagrams (when applicable)

Web Content Extraction

• Blog Posts: Extracts article text, code blocks (with syntax highlighting preserved), and inline documentation
• Documentation Sites: Navigates multi-page documentation, extracts API specifications, and example code
• Tutorials: Identifies step-by-step instructions and corresponding code snippets
• GitHub READMEs: Parses markdown with special handling for badges, links, and code fences

Techniques Used:

• Trafilatura for main content extraction (removes boilerplate)
• BeautifulSoup for structured HTML parsing
• CSS selector-based code block detection
• Metadata extraction (author, date, tags)

Jupyter Notebook Parsing

• Code Cells: Extracts executable code with cell ordering preserved
• Markdown Cells: Processes explanatory text with formatting
• Outputs: Captures cell outputs including plots, tables, and error messages
• Metadata: Extracts kernel information and dependencies

Techniques Used:

• Native nbformat parsing
• Dependency detection from import statements
• Output analysis for result validation
• Cell type classification

Markdown & Plain Text

• Markdown Files: Full CommonMark and GFM support
• Code Blocks: Language detection from fence annotations
• Inline Code: Extraction and classification
• Links & References: Preservation for context

Techniques Used:

• Mistune parser for markdown
• Regex-based code block extraction
• Link resolution for external references
• Metadata extraction from YAML front matter

Intelligent Content Analysis

Algorithm Detection

The skill uses sophisticated pattern matching and semantic analysis to identify:

• Pseudocode: Recognizes common pseudocode conventions (if/else, for/while, procedure definitions)
• Mathematical Notation: Interprets algorithms described using mathematical formulas
• Natural Language Descriptions: Extracts algorithmic logic from prose descriptions
• Complexity Analysis: Identifies time and space complexity specifications

Detection Strategies:

1. Structural Analysis: Looks for numbered steps, indentation patterns, and control flow keywords
2. Mathematical Patterns: Identifies summations, products, set operations, and recursive definitions
3. Keyword Recognition: Detects algorithm-specific terminology (sort, search, optimize, iterate)
4. Context Awareness: Uses surrounding text to disambiguate and clarify intent

Architecture Identification

Recognizes and extracts architectural patterns including:

• Design Patterns: Singleton, Factory, Observer, Strategy, etc.
• System Architectures: Microservices, client-server, event-driven, layered
• Data Flow Patterns: ETL pipelines, stream processing, batch processing
• Component Diagrams: Identifies components and their relationships from textual descriptions

Identification Methods:

1. Pattern Vocabulary: Maintains a database of architectural terms and their characteristics
2. Relationship Extraction: Identifies connections between components (uses, extends, implements)
3. Diagram Interpretation: When diagrams are described textually, reconstructs the architecture
4. Technology Stack Detection: Identifies mentioned frameworks and libraries

Dependency Extraction

Automatically identifies and catalogs:

• Libraries & Frameworks: Mentioned tools and their versions
• APIs: External services and their endpoints
• Data Sources: Databases, file formats, data APIs
• System Requirements: Operating systems, runtime versions, hardware requirements

Extraction Techniques:

1. Import Statement Analysis: Parses code examples for import/require statements
2. Inline Mentions: Detects "using X" or "built with Y" patterns
3. Version Specifications: Extracts version numbers and compatibility requirements
4. Installation Instructions: Identifies package manager commands and configuration steps

Domain Classification

Classifies the content into specific domains to guide language selection:

• Machine Learning: TensorFlow, PyTorch, scikit-learn mentions
• Web Development: React, Node.js, REST API patterns
• Systems Programming: Performance, concurrency, memory management discussions
• Data Science: Pandas, NumPy, statistical analysis
• Scientific Computing: Numerical methods, simulations, mathematical modeling
• DevOps: Infrastructure, deployment, orchestration

Classification Process:

1. Keyword Density Analysis: Measures frequency of domain-specific terms
2. Technology Stack Analysis: Infers domain from mentioned tools
3. Problem Space Analysis: Identifies the type of problem being solved
4. Methodology Detection: Recognizes domain-specific methodologies (e.g., machine learning workflows)

Multi-Language Code Generation

Language Selection Logic

The skill uses a decision tree to select the optimal programming language:

IF domain == "machine_learning" AND mentions(pandas, numpy, sklearn):
    SELECT Python
ELSE IF domain == "web" AND mentions(react, node):
    SELECT JavaScript/TypeScript
ELSE IF domain == "systems" AND mentions(performance, concurrency):
    SELECT Rust OR Go
ELSE IF domain == "scientific" AND mentions(numerical, simulation):
    SELECT Julia OR Python
ELSE IF domain == "data_engineering" AND mentions(big_data, spark):
    SELECT Scala OR Python
ELSE:
    SELECT Python (default - most versatile)

Selection Criteria:

1. Explicit Mentions: If the article explicitly states a language, use it
2. Domain Best Practices: Match language to domain conventions
3. Library Availability: Consider if required libraries exist in the language
4. Performance Requirements: High-performance needs may favor compiled languages
5. Ecosystem Maturity: Prefer languages with mature ecosystems for the domain

Supported Languages

Python

Use Cases: Machine learning, data science, scripting, general-purpose prototyping Generated Features:

• Type hints (PEP 484 compatible)
• Docstrings (Google or NumPy style)
• Virtual environment setup
• requirements.txt with pinned versions
• pytest test suite structure
• Logging configuration
• CLI interface with argparse

JavaScript/TypeScript

Use Cases: Web applications, Node.js backends, REST APIs Generated Features:

• Modern ES6+ syntax or TypeScript
• package.json with scripts
• ESLint configuration
• Jest test suite
• Express.js setup (if API)
• Frontend framework integration (if applicable)
• Environment variable management

Rust

Use Cases: Systems programming, high-performance tools, concurrent applications Generated Features:

• Cargo.toml configuration
• Module structure (lib.rs, main.rs)
• Error handling with Result types
• Documentation comments (///)
• Unit tests with #[cfg(test)]
• Benchmarks with criterion
• CI/CD templates

Go

Use Cases: Microservices, CLI tools, concurrent systems Generated Features:

• go.mod dependency management
• Package structure
• Interface definitions
• Error handling patterns
• Table-driven tests
• goroutine usage for concurrency
• Standard library preference

Julia

Use Cases: Scientific computing, numerical analysis, high-performance math Generated Features:

• Project.toml configuration
• Module structure
• Multiple dispatch examples
• Vectorized operations
• Test suite with Test.jl
• Documentation with Documenter.jl
• Performance annotations

Other Languages (Java, C++)

Generated on Demand when explicitly mentioned or domain-required

Prototype Quality Standards

Every generated prototype adheres to strict quality standards:

Code Quality

• No Placeholders: All functions are fully implemented
• Type Safety: Type hints (Python), type annotations (TypeScript), or strong typing (Rust, Go)
• Error Handling: Comprehensive try/catch, Result types, or error return values
• Logging: Structured logging with appropriate levels
• Configuration: Environment variables or config files (never hardcoded values)

Documentation Quality

• Inline Comments: Explain non-obvious logic
• Function Documentation: Parameters, return values, exceptions
• Module Documentation: Purpose and usage overview
• README: Installation, usage, examples, troubleshooting
• Source Attribution: Links back to the original article

Testing Quality

• Unit Tests: Core logic coverage
• Example Tests: Demonstrate usage patterns
• Edge Cases: Boundary conditions and error scenarios
• Test Data: Sample inputs included where appropriate

Project Structure

• Standard Layout: Follows language conventions (src/, tests/, docs/)
• Dependency Management: requirements.txt, package.json, Cargo.toml, etc.
• Version Control: .gitignore with language-specific patterns
• License: MIT license included (can be customized)

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Architecture & Design

System Architecture

The Article-to-Prototype Skill follows a modular pipeline architecture:

Input → Extraction → Analysis → Generation → Output
  ↓         ↓          ↓           ↓          ↓
Format    Content   Technical   Code Gen   Complete
Detection Structure Concepts   & Docs     Prototype

Each stage is independent and replaceable, allowing for:

• Parallel Processing: Multiple articles can be processed simultaneously
• Caching: Extracted content can be cached for re-analysis
• Extensibility: New formats or languages can be added without changing other components
• Testing: Each component can be tested in isolation

Component Diagram

┌─────────────────────────────────────────────────────────┐
│                    Main Orchestrator                     │
│                      (main.py)                           │
└────────┬────────────────────────────────────────┬───────┘
         │                                        │
         ▼                                        ▼
┌─────────────────────┐                ┌─────────────────────┐
│   Format Detector   │                │   AgentDB Bridge    │
│                     │                │   (Learning Layer)  │
└────────┬────────────┘                └─────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────┐
│                    Extractors Layer                      │
├─────────────┬──────────────┬──────────────┬─────────────┤
│ PDF         │ Web          │ Notebook     │ Markdown    │
│ Extractor   │ Extractor    │ Extractor    │ Extractor   │
└─────────────┴──────────────┴──────────────┴─────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────┐
│                    Analyzers Layer                       │
├──────────────────────────────┬──────────────────────────┤
│ Content Analyzer             │ Code Detector            │
│ - Algorithm detection        │ - Pseudocode parsing     │
│ - Architecture identification│ - Language hints         │
│ - Domain classification      │ - Dependency extraction  │
└──────────────────────────────┴──────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────┐
│                   Generators Layer                       │
├──────────────────────────────┬──────────────────────────┤
│ Language Selector            │ Prototype Generator      │
│ - Decision logic             │ - Code synthesis         │
│ - Compatibility checking     │ - Documentation gen      │
└──────────────────────────────┴──────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────┐
│                    Output Layer                          │
│  - Generated code files                                  │
│  - README.md with context                                │
│  - Dependency manifest                                   │
│  - Test suite                                            │
└─────────────────────────────────────────────────────────┘

Data Flow

1. Input Normalization

   • User provides file path, URL, or direct text
   • Format detector identifies the type
   • Appropriate extractor is selected

2. Content Extraction

   • Extractor processes the input
   • Produces structured content object:

     {
       "title": str,
       "sections": List[Section],
       "code_blocks": List[CodeBlock],
       "metadata": Dict[str, Any],
       "references": List[str]
     }
     

3. Semantic Analysis

   • Content analyzer processes structured content
   • Produces analysis object:

     {
       "algorithms": List[Algorithm],
       "architectures": List[Architecture],
       "dependencies": List[Dependency],
       "domain": str,
       "complexity": str
     }
     

4. Generation Planning

   • Language selector chooses optimal language
   • Prototype generator plans file structure
   • Produces generation plan:

     {
       "language": str,
       "project_structure": Dict[str, str],
       "dependencies": List[str],
       "entry_point": str
     }
     

5. Code Generation

   • Generates each file according to plan
   • Applies language-specific formatting
   • Includes comprehensive documentation

6. Output Assembly

   • Creates project directory
   • Writes all files
   • Generates README with source attribution
   • Returns path to generated prototype

Caching Strategy

The skill implements multi-level caching for performance:

1. Extracted Content Cache: Stores parsed content for 24 hours

   • Key: Hash of input (file path or URL)
   • Value: Structured content object
   • Benefit: Avoid re-downloading or re-parsing

2. Analysis Cache: Stores analysis results for 12 hours

   • Key: Hash of structured content
   • Value: Analysis object
   • Benefit: Enable rapid re-generation in different languages

3. AgentDB Learning Cache: Permanent storage of successful patterns

   • Key: Content fingerprint
   • Value: Optimal language, common issues, quality metrics
   • Benefit: Progressive improvement over time

---

Detailed Component Specifications

Extractor Components

PDF Extractor (scripts/extractors/pdf_extractor.py)

Responsibility: Extract text, structure, and metadata from PDF documents.

Key Features:

• Multi-strategy approach (tries PyPDF2, falls back to pdfplumber)
• Layout analysis for column detection
• Font-based heading detection
• Code block identification (monospace fonts, background boxes)
• Equation extraction (preserves LaTeX when available)
• Table extraction with structure preservation
• Figure caption extraction

Public Interface:

class PDFExtractor:
    def extract(self, pdf_path: str) -> ExtractedContent:
        """
        Extracts content from a PDF file.

        Args:
            pdf_path: Path to the PDF file

        Returns:
            ExtractedContent object with structured data

        Raises:
            PDFExtractionError: If extraction fails
        """
        pass

    def extract_metadata(self, pdf_path: str) -> Dict[str, Any]:
        """Extracts PDF metadata (title, author, creation date)"""
        pass

    def extract_sections(self, pdf_path: str) -> List[Section]:
        """Extracts document sections with headings"""
        pass

Implementation Details:

• Uses pdfplumber as primary library (better layout analysis)
• Falls back to PyPDF2 for compatibility
• Implements custom heuristics for code detection:
  • Monospace font usage
  • Indentation patterns
  • Background color/shading
  • Line numbering
• Preserves page numbers for reference
• Handles encrypted PDFs (prompts for password if needed)

Error Handling:

• Corrupted PDF detection
• Unsupported encryption handling
• Partial extraction on errors (returns what was successfully extracted)
• Detailed error messages for troubleshooting

Web Extractor (scripts/extractors/web_extractor.py)

Responsibility: Fetch and extract content from web pages and documentation.

Key Features:

• Boilerplate removal (navigation, ads, footers)
• Code block extraction with language detection
• Multi-page documentation crawling
• Respect for robots.txt
• Rate limiting
• Caching for repeated requests

Public Interface:

class WebExtractor:
    def extract(self, url: str) -> ExtractedContent:
        """
        Extracts content from a web page.

        Args:
            url: URL to fetch and extract

        Returns:
            ExtractedContent object with structured data

        Raises:
            WebExtractionError: If fetching or parsing fails
        """
        pass

    def extract_code_blocks(self, url: str) -> List[CodeBlock]:
        """Extracts only code blocks from the page"""
        pass

    def crawl_documentation(self, base_url: str, max_pages: int = 10) -> List[ExtractedContent]:
        """Crawls multi-page documentation"""
        pass

Implementation Details:

• Primary strategy: trafilatura (excellent at main content extraction)
• Fallback: BeautifulSoup with custom selectors
• Code block detection:
  • <pre><code> tags
  • <div class="highlight"> patterns
  • Prism.js/highlight.js structures
  • Language class extraction (language-python, etc.)
• Metadata extraction from <meta> tags
• Link extraction for related content
• Image alt text extraction for diagram context

Error Handling:

• Network error recovery with retries
• 404/403 handling
• Redirect following (with limit)
• Timeout configuration
• Content-Type validation

Notebook Extractor (scripts/extractors/notebook_extractor.py)

Responsibility: Parse Jupyter notebooks and extract code, markdown, and outputs.

Key Features:

• Native nbformat parsing
• Cell type classification
• Code dependency detection
• Output capture (text, images, errors)
• Kernel metadata extraction
• Cell execution order preservation

Public Interface:

class NotebookExtractor:
    def extract(self, notebook_path: str) -> ExtractedContent:
        """
        Extracts content from a Jupyter notebook.

        Args:
            notebook_path: Path to the .ipynb file

        Returns:
            ExtractedContent object with cells and outputs

        Raises:
            NotebookExtractionError: If parsing fails
        """
        pass

    def extract_code_cells(self, notebook_path: str) -> List[CodeCell]:
        """Extracts only code cells"""
        pass

    def extract_dependencies(self, notebook_path: str) -> List[str]:
        """Extracts imported libraries and dependencies"""
        pass

Implementation Details:

• Uses nbformat library for parsing
• Handles both notebook format versions (v3 and v4)
• Extracts imports from code cells:

  import re
  pattern = r'^(?:from\s+(\S+)\s+)?import\s+(\S+)'
  

• Analyzes outputs for result validation
• Preserves cell metadata (execution count, timing)
• Handles embedded images (base64 encoded)

Error Handling:

• Invalid JSON handling
• Missing kernel specification handling
• Corrupted cell recovery
• Version compatibility warnings

Markdown Extractor (scripts/extractors/markdown_extractor.py)

Responsibility: Parse markdown files and extract structure and content.

Key Features:

• Full CommonMark and GFM support
• YAML front matter parsing
• Code fence language detection
• Nested list handling
• Table extraction
• Link resolution

Public Interface:

class MarkdownExtractor:
    def extract(self, markdown_path: str) -> ExtractedContent:
        """
        Extracts content from a markdown file.

        Args:
            markdown_path: Path to the .md file

        Returns:
            ExtractedContent object with structured content

        Raises:
            MarkdownExtractionError: If parsing fails
        """
        pass

    def extract_code_blocks(self, markdown_path: str) -> List[CodeBlock]:
        """Extracts only code blocks with language annotations"""
        pass

Implementation Details:

• Uses mistune parser (fast and CommonMark compliant)
• YAML front matter extraction using PyYAML
• Code fence parsing with language detection:

  ```python
  # Language is detected from fence annotation
  

• Heading hierarchy extraction for structure
• Link resolution (converts relative to absolute)
• Inline code backtick handling

Error Handling:

• Malformed markdown recovery
• YAML parsing errors
• Binary file detection (and rejection)
• Encoding detection and handling

Analyzer Components

Content Analyzer (scripts/analyzers/content_analyzer.py)

Responsibility: Semantic analysis of extracted content to identify technical concepts.

Key Features:

• Algorithm detection and extraction
• Architecture pattern recognition
• Domain classification
• Complexity assessment
• Dependency identification
• Methodology extraction

Public Interface:

class ContentAnalyzer:
    def analyze(self, content: ExtractedContent) -> AnalysisResult:
        """
        Analyzes extracted content for technical concepts.

        Args:
            content: ExtractedContent object from extractor

        Returns:
            AnalysisResult with detected algorithms, architectures, etc.
        """
        pass

    def detect_algorithms(self, content: ExtractedContent) -> List[Algorithm]:
        """Detects and extracts algorithms"""
        pass

    def classify_domain(self, content: ExtractedContent) -> str:
        """Classifies the content domain"""
        pass

Algorithm Detection Strategy:

1. Pattern Matching: Look for algorithmic keywords (sort, search, traverse, optimize)
2. Structure Analysis: Identify step-by-step procedures
3. Complexity Indicators: Find Big-O notation, complexity analysis
4. Pseudocode Recognition: Detect pseudocode conventions

Architecture Recognition Strategy:

1. Pattern Database: Maintain library of known patterns (Singleton, Factory, etc.)
2. Keyword Analysis: Identify architectural terms (microservice, layered, event-driven)
3. Component Relationships: Extract relationships (uses, extends, implements)
4. Diagram Interpretation: Parse textual descriptions of architectures

Domain Classification:

DOMAIN_INDICATORS = {
    "machine_learning": [
        "neural network", "training", "model", "dataset",
        "accuracy", "loss function", "tensorflow", "pytorch"
    ],
    "web_development": [
        "HTTP", "REST", "API", "frontend", "backend",
        "server", "client", "route", "endpoint"
    ],
    "systems_programming": [
        "concurrency", "thread", "process", "memory",
        "performance", "optimization", "low-level"
    ],
    # ... more domains
}

Output Format:

@dataclass
class AnalysisResult:
    algorithms: List[Algorithm]
    architectures: List[Architecture]
    dependencies: List[str]
    domain: str
    complexity: str  # "simple", "moderate", "complex"
    confidence: float  # 0.0 to 1.0
    metadata: Dict[str, Any]

Code Detector (scripts/analyzers/code_detector.py)

Responsibility: Detect and analyze code fragments, pseudocode, and language hints.

Key Features:

• Pseudocode to formal code translation planning
• Programming language detection from hints
• Code pattern recognition (loops, conditionals, functions)
• Syntax validation
• Import/dependency extraction

Public Interface:

class CodeDetector:
    def detect_code_fragments(self, content: ExtractedContent) -> List[CodeFragment]:
        """Detects code and pseudocode in content"""
        pass

    def detect_language_hints(self, content: ExtractedContent) -> List[str]:
        """Detects mentioned programming languages"""
        pass

    def extract_pseudocode(self, text: str) -> List[PseudocodeBlock]:
        """Extracts and structures pseudocode"""
        pass

Pseudocode Detection Patterns:

- "Algorithm X:"
- Numbered steps (1., 2., 3. or Step 1:, Step 2:)
- Indented control structures (IF, WHILE, FOR)
- Mathematical notation with algorithmic context
- "Procedure" or "Function" headers

Language Hint Detection:

• Explicit mentions: "implemented in Python", "using JavaScript"
• Code block language annotations
• Library/framework mentions
• Ecosystem indicators (npm → JavaScript, pip → Python)

Generator Components

Language Selector (scripts/generators/language_selector.py)

Responsibility: Select the optimal programming language for the prototype.

Selection Algorithm:

def select_language(analysis: AnalysisResult) -> str:
    # Priority 1: Explicit mention
    if analysis.explicit_language:
        return analysis.explicit_language

    # Priority 2: Domain best practices
    domain_language_map = {
        "machine_learning": "python",
        "web_development": "typescript",
        "systems_programming": "rust",
        "scientific_computing": "julia",
        "data_engineering": "python"
    }
    if analysis.domain in domain_language_map:
        candidate = domain_language_map[analysis.domain]

        # Verify required libraries exist
        if check_library_availability(analysis.dependencies, candidate):
            return candidate

    # Priority 3: Dependency-driven selection
    language_scores = score_by_dependencies(analysis.dependencies)
    if max(language_scores.values()) > 0.7:
        return max(language_scores, key=language_scores.get)

    # Default: Python (most versatile)
    return "python"

Scoring Logic:

def score_by_dependencies(dependencies: List[str]) -> Dict[str, float]:
    scores = {lang: 0.0 for lang in SUPPORTED_LANGUAGES}

    for dep in dependencies:
        if dep in LIBRARY_TO_LANGUAGE:
            lang = LIBRARY_TO_LANGUAGE[dep]
            scores[lang] += 1.0

    # Normalize
    total = sum(scores.values())
    if total > 0:
        scores = {k: v/total for k, v in scores.items()}

    return scores

Prototype Generator (scripts/generators/prototype_generator.py)

Responsibility: Generate complete, production-quality code prototypes.

Generation Process:

1. Project Structure Planning: Determine files and directories
2. Dependency Resolution: Identify all required libraries
3. Code Synthesis: Generate implementation code
4. Test Generation: Create test suite
5. Documentation Creation: Write README and inline docs
6. Configuration Files: Generate language-specific configs

Public Interface:

class PrototypeGenerator:
    def generate(
        self,
        analysis: AnalysisResult,
        language: str,
        output_dir: str
    ) -> GeneratedPrototype:
        """
        Generates a complete prototype project.

        Args:
            analysis: Analysis result from ContentAnalyzer
            language: Selected programming language
            output_dir: Directory to write output files

        Returns:
            GeneratedPrototype with file paths and metadata
        """
        pass

Code Quality Enforcement:

• Type Safety: Adds type hints (Python), type annotations (TypeScript), or strong typing
• Error Handling: Wraps operations in try/catch or Result types
• Logging: Adds structured logging at appropriate levels
• Documentation: Generates docstrings/comments for all public interfaces
• Testing: Creates unit tests for core functionality

Template System: The generator uses a template system for each language:

templates/
├── python/
│   ├── main.py.template
│   ├── requirements.txt.template
│   ├── README.md.template
│   └── test_main.py.template
├── typescript/
│   ├── index.ts.template
│   ├── package.json.template
│   └── ...
└── ...

Templates use Jinja2-style variable substitution:

# main.py.template
"""
{{ project_name }}

Generated from: {{ source_url }}
Domain: {{ domain }}
"""

import logging
{% for dependency in dependencies %}
import {{ dependency }}
{% endfor %}

# ... rest of template

---

Extraction Pipeline

Pipeline Stages

The extraction pipeline follows a well-defined sequence:

Input → Detection → Extraction → Structuring → Validation → Output

Stage 1: Format Detection

• Analyze input to determine format
• Check file extension
• Read magic bytes for binary formats
• Validate URL structure

def detect_format(input_path: str) -> str:
    if input_path.startswith("http"):
        return "url"
    ext = Path(input_path).suffix.lower()
    if ext == ".pdf":
        return "pdf"
    elif ext == ".ipynb":
        return "notebook"
    elif ext in [".md", ".markdown"]:
        return "markdown"
    elif ext == ".txt":
        return "text"
    else:
        raise UnsupportedFormatError(f"Unknown format: {ext}")

Stage 2: Extraction

• Select appropriate extractor
• Apply format-specific parsing
• Handle errors gracefully
• Collect metadata

Stage 3: Structuring

• Normalize extracted content into common format
• Identify sections and hierarchies
• Separate code from prose
• Build content graph

Common Content Structure:

@dataclass
class ExtractedContent:
    title: str
    sections: List[Section]
    code_blocks: List[CodeBlock]
    metadata: Dict[str, Any]
    source_url: Optional[str]
    extraction_date: datetime

@dataclass
class Section:
    heading: str
    level: int  # 1, 2, 3, etc.
    content: str
    subsections: List['Section']

@dataclass
class CodeBlock:
    language: Optional[str]
    code: str
    line_number: Optional[int]
    context: str  # Surrounding text for context

Stage 4: Validation

• Verify content quality
• Check for extraction errors
• Validate structure integrity
• Compute confidence score

def validate_extraction(content: ExtractedContent) -> ValidationResult:
    issues = []

    # Check for minimum content
    if len(content.sections) == 0:
        issues.append("No sections extracted")

    # Check for code presence (if expected)
    if is_technical_content(content) and len(content.code_blocks) == 0:
        issues.append("No code blocks found in technical content")

    # Check for metadata completeness
    required_metadata = ["title", "source"]
    missing = [k for k in required_metadata if k not in content.metadata]
    if missing:
        issues.append(f"Missing metadata: {missing}")

    confidence = 1.0 - (len(issues) * 0.1)
    return ValidationResult(valid=len(issues) == 0, issues=issues, confidence=confidence)

Stage 5: Output

• Return structured content
• Cache for future use
• Log extraction metrics
• Update AgentDB with patterns

---

Analysis Methodology

Semantic Analysis Pipeline

Content → Tokenization → NER → Pattern Matching → Classification → Output

Tokenization & Preprocessing

• Sentence segmentation
• Word tokenization
• Stop word removal (selective - preserve technical terms)
• Lemmatization for better matching

Named Entity Recognition (NER)

While we don't use a full NER model, we implement domain-specific entity recognition:

• Algorithms: QuickSort, Dijkstra's, Backpropagation
• Architectures: MVC, Microservices, Client-Server
• Technologies: TensorFlow, React, PostgreSQL
• Concepts: Concurrency, Recursion, Optimization

Pattern Matching

Regular expressions and structural patterns for:

• Algorithm descriptions
• Pseudocode blocks
• Complexity analysis (O(n), O(log n))
• Dependency mentions

Example Patterns:

ALGORITHM_PATTERNS = [
    r'Algorithm\s+\d+:?\s+(.+)',
    r'(?:The|This)\s+algorithm\s+(.+?)\.',
    r'(?:function|procedure)\s+(\w+)\s*\(',
]

COMPLEXITY_PATTERNS = [
    r'O\([^)]+\)',
    r'time complexity[:\s]+(.+)',
    r'space complexity[:\s]+(.+)',
]

Domain Classification

Uses TF-IDF vectorization on domain-specific vocabularies:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Precomputed domain vocabularies
DOMAIN_TEXTS = {
    "machine_learning": "...",  # Representative text
    "web_development": "...",
    # ...
}

def classify_domain(content: str) -> str:
    vectorizer = TfidfVectorizer()
    all_texts = list(DOMAIN_TEXTS.values()) + [content]
    tfidf_matrix = vectorizer.fit_transform(all_texts)

    content_vector = tfidf_matrix[-1]
    domain_vectors = tfidf_matrix[:-1]

    similarities = cosine_similarity(content_vector, domain_vectors)[0]
    best_domain_idx = similarities.argmax()

    return list(DOMAIN_TEXTS.keys())[best_domain_idx]

Algorithm Extraction

Multi-Strategy Approach:

1. Explicit Algorithms: Look for "Algorithm X:" headers
2. Pseudocode: Detect indented procedural descriptions
3. Inline Descriptions: Extract from prose using NLP
4. Code Examples: Analyze provided code for algorithmic patterns

Extraction Example:

Input: "The sorting algorithm works as follows: 1. Compare adjacent elements.
2. Swap if they're in the wrong order. 3. Repeat until the list is sorted."

Output:
Algorithm(
    name="Bubble Sort" (inferred),
    steps=[
        "Compare adjacent elements",
        "Swap if they're in the wrong order",
        "Repeat until the list is sorted"
    ],
    complexity="O(n^2)" (inferred from pattern),
    pseudocode=None
)

Dependency Graph Construction

Build a dependency graph to understand relationships:

@dataclass
class DependencyGraph:
    nodes: List[Dependency]  # Libraries, APIs, services
    edges: List[Tuple[str, str, str]]  # (from, to, relationship)

def build_dependency_graph(content: ExtractedContent) -> DependencyGraph:
    graph = DependencyGraph(nodes=[], edges=[])

    # Extract direct dependencies from imports
    for code_block in content.code_blocks:
        imports = extract_imports(code_block.code)
        graph.nodes.extend(imports)

    # Extract mentioned dependencies from text
    for section in content.sections:
        mentioned = extract_mentioned_dependencies(section.content)
        graph.nodes.extend(mentioned)

    # Build relationships (e.g., "A requires B")
    graph.edges = infer_relationships(graph.nodes, content)

    return graph

---

Code Generation Strategy

Generation Principles

1. Completeness: No TODOs, no placeholders, fully functional
2. Clarity: Readable code with meaningful variable names
3. Correctness: Type-safe, error-handled, tested
4. Context Preservation: Comments linking back to source material
5. Best Practices: Follow language idioms and conventions

Language-Specific Generation

Python Generation

def generate_python_project(analysis: AnalysisResult, output_dir: str):
    # Project structure
    create_directory_structure(output_dir, [
        "src/",
        "tests/",
        "docs/",
    ])

    # Generate main module
    main_code = generate_python_main(analysis)
    write_file(f"{output_dir}/src/main.py", main_code)

    # Generate requirements.txt
    requirements = generate_requirements(analysis.dependencies)
    write_file(f"{output_dir}/requirements.txt", requirements)

    # Generate tests
    test_code = generate_python_tests(analysis)
    write_file(f"{output_dir}/tests/test_main.py", test_code)

    # Generate README
    readme = generate_readme(analysis, "python")
    write_file(f"{output_dir}/README.md", readme)

    # Generate pyproject.toml
    pyproject = generate_pyproject_toml(analysis)
    write_file(f"{output_dir}/pyproject.toml", pyproject)

Python Code Template:

"""
{module_name}

Generated from: {source_url}
Domain: {domain}

{description}
"""

import logging
from typing import List, Dict, Any, Optional
{additional_imports}

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

{generated_classes}

{generated_functions}

def main():
    """Main entry point"""
    logger.info("Starting {project_name}")
    {main_logic}

if __name__ == "__main__":
    main()

TypeScript Generation

/**
 * {module_name}
 *
 * Generated from: {source_url}
 * Domain: {domain}
 *
 * {description}
 */

{imports}

{interfaces}

{classes}

{functions}

// Main execution
if (require.main === module) {
  main();
}

export { {exports} };

Rust Generation

//! {module_name}
//!
//! Generated from: {source_url}
//! Domain: {domain}
//!
//! {description}

{use_statements}

{structs}

{implementations}

{functions}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    {main_logic}
    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;

    {test_functions}
}

Documentation Generation

Every generated project includes comprehensive documentation:

README Structure:

# {Project Name}

> Generated from [{source_title}]({source_url})

## Overview
{Brief description extracted from article}

## Installation
{Language-specific installation instructions}

## Usage
{Code examples demonstrating usage}

## Implementation Details
{Links between code and article sections}

## Testing
{How to run tests}

## Source Attribution
- Original Article: [{title}]({url})
- Extraction Date: {date}
- Generated by: Article-to-Prototype Skill v1.0

## License
MIT License (see LICENSE file)

Test Generation

Automatically generates tests based on analysis:

def generate_tests(analysis: AnalysisResult, language: str) -> str:
    tests = []

    # Test for each detected algorithm
    for algo in analysis.algorithms:
        test = generate_algorithm_test(algo, language)
        tests.append(test)

    # Test for main functionality
    tests.append(generate_integration_test(analysis, language))

    # Test for error handling
    tests.append(generate_error_handling_test(analysis, language))

    return format_test_suite(tests, language)

---

Usage Examples

Example 1: Implementing an Algorithm from a PDF Paper

Input:

# User command in Claude Code
extract from paper "path/to/dijkstra_paper.pdf" and implement in Python

Processing:

1. PDF Extractor reads the paper
2. Content Analyzer detects Dijkstra's algorithm
3. Language Selector chooses Python (explicitly requested)
4. Prototype Generator creates implementation

Output:

dijkstra-implementation/
├── src/
│   ├── dijkstra.py          # Implementation with type hints
│   ├── graph.py             # Graph data structure
│   └── utils.py             # Helper functions
├── tests/
│   ├── test_dijkstra.py     # Unit tests
│   └── test_graph.py
├── requirements.txt          # numpy, pytest
├── README.md                # Usage and explanation
└── LICENSE

Generated Code Sample (src/dijkstra.py):

"""
Dijkstra's Shortest Path Algorithm

Implemented from: "A Note on Two Problems in Connexion with Graphs"
By E. W. Dijkstra (1959)

This module implements Dijkstra's algorithm for finding the shortest path
in a weighted graph with non-negative edge weights.
"""

import heapq
from typing import Dict, List, Tuple, Optional
import logging

logger = logging.getLogger(__name__)

def dijkstra(
    graph: Dict[str, List[Tuple[str, float]]],
    start: str,
    end: Optional[str] = None
) -> Tuple[Dict[str, float], Dict[str, Optional[str]]]:
    """
    Find shortest paths from start node using Dijkstra's algorithm.

    Args:
        graph: Adjacency list representation {node: [(neighbor, weight), ...]}
        start: Starting node
        end: Optional ending node (if provided, returns early upon reaching)

    Returns:
        Tuple of (distances, predecessors) where:
        - distances: Dict mapping node to shortest distance from start
        - predecessors: Dict mapping node to its predecessor in shortest path

    Raises:
        ValueError: If start node not in graph

    Time Complexity: O((V + E) log V) with binary heap
    Space Complexity: O(V)

    Example:
        >>> graph = {
        ...     'A': [('B', 1), ('C', 4)],
        ...     'B': [('C', 2), ('D', 5)],
        ...     'C': [('D', 1)],
        ...     'D': []
        ... }
        >>> distances, _ = dijkstra(graph, 'A')
        >>> distances['D']
        4
    """
    if start not in graph:
        raise ValueError(f"Start node '{start}' not found in graph")

    # Initialize distances and predecessors
    distances: Dict[str, float] = {node: float('inf') for node in graph}
    distances[start] = 0
    predecessors: Dict[str, Optional[str]] = {node: None for node in graph}

    # Priority queue: (distance, node)
    pq: List[Tuple[float, str]] = [(0, start)]
    visited: set = set()

    logger.info(f"Starting Dijkstra's algorithm from node '{start}'")

    while pq:
        current_distance, current_node = heapq.heappop(pq)

        # Early termination if we reached the end node
        if end and current_node == end:
            logger.info(f"Reached end node '{end}' with distance {current_distance}")
            break

        # Skip if already visited
        if current_node in visited:
            continue

        visited.add(current_node)
        logger.debug(f"Visiting node '{current_node}' at distance {current_distance}")

        # Explore neighbors
        for neighbor, weight in graph[current_node]:
            if neighbor in visited:
                continue

            new_distance = current_distance + weight

            # Relaxation step (as described in the original paper)
            if new_distance < distances[neighbor]:
                distances[neighbor] = new_distance
                predecessors[neighbor] = current_node
                heapq.heappush(pq, (new_distance, neighbor))
                logger.debug(f"Updated distance to '{neighbor}': {new_distance}")

    return distances, predecessors

def reconstruct_path(
    predecessors: Dict[str, Optional[str]],
    start: str,
    end: str
) -> Optional[List[str]]:
    """
    Reconstruct shortest path from predecessors dictionary.

    Args:
        predecessors: Dict from dijkstra() mapping nodes to predecessors
        start: Start node
        end: End node

    Returns:
        List of nodes in shortest path from start to end, or None if no path exists
    """
    if predecessors[end] is None and end != start:
        return None  # No path exists

    path = []
    current = end

    while current is not None:
        path.append(current)
        current = predecessors[current]

    path.reverse()
    return path

Example 2: Building a Web API from Documentation

Input:

create prototype from "https://docs.example.com/rest-api-tutorial"

Processing:

1. Web Extractor fetches and parses the page
2. Content Analyzer identifies REST API patterns and endpoints
3. Language Selector chooses TypeScript/Node.js (web domain)
4. Prototype Generator creates Express.js server

Output:

rest-api-prototype/
├── src/
│   ├── index.ts             # Main server
│   ├── routes/
│   │   ├── users.ts
│   │   └── products.ts
│   ├── middleware/
│   │   ├── auth.ts
│   │   └── errorHandler.ts
│   └── types/
│       └── index.ts
├── tests/
│   └── api.test.ts
├── package.json
├── tsconfig.json
├── .env.example
└── README.md

Example 3: Implementing ML Algorithm from Jupyter Notebook

Input:

implement algorithm from "research_notebook.ipynb"

Processing:

1. Notebook Extractor parses cells and extracts code
2. Content Analyzer identifies ML pipeline
3. Language Selector chooses Python (ML domain + existing Python code)
4. Prototype Generator creates standalone script

Output:

ml-algorithm-implementation/
├── src/
│   ├── model.py             # Model implementation
│   ├── preprocessing.py     # Data preprocessing
│   ├── training.py          # Training loop
│   └── evaluation.py        # Metrics and evaluation
├── tests/
│   └── test_model.py
├── requirements.txt         # scikit-learn, pandas, numpy
├── data/
│   └── sample_data.csv
└── README.md

---

Quality Standards

Code Quality Checklist

Every generated prototype must pass these quality gates:

• No Placeholders: All functions fully implemented
• Type Annotations: Type hints (Python), types (TypeScript), strong typing (Rust/Go)
• Error Handling: Try/catch, Result types, or error returns for all external operations
• Logging: Structured logging at INFO, DEBUG, and ERROR levels
• Documentation: Docstrings/comments for all public interfaces
• Tests: Unit tests with >80% coverage of core logic
• Dependencies: All listed in manifest with version pins
• README: Complete with installation, usage, and examples
• License: Included (default MIT)
• Source Attribution: Links to original article maintained

Validation Process

Before outputting a prototype, the generator runs validation:

def validate_prototype(prototype_dir: str) -> ValidationResult:
    checks = [
        check_all_files_exist(prototype_dir),
        check_no_placeholders(prototype_dir),
        check_syntax_valid(prototype_dir),
        check_tests_present(prototype_dir),
        check_documentation_complete(prototype_dir),
        check_dependencies_valid(prototype_dir),
    ]

    passed = all(check.passed for check in checks)
    issues = [check.message for check in checks if not check.passed]

    return ValidationResult(passed=passed, issues=issues)

If validation fails, the generator retries with corrections or reports the issue to the user.

---

Performance & Optimization

Caching Strategy

Three-tier caching system:

1. L1 Cache (Memory): In-memory cache for current session

   • Stores extracted content objects
   • Expires on skill termination
   • Instant access (< 1ms)

2. L2 Cache (Disk): Local file cache

   • Stores extracted content in JSON format
   • 24-hour expiration
   • Fast access (~10ms)

3. L3 Cache (AgentDB): Persistent learning cache

   • Stores successful patterns and analyses
   • Never expires (evolves over time)
   • Network access (~100-500ms)

Parallel Processing

The skill supports parallel processing for batch operations:

from concurrent.futures import ThreadPoolExecutor, as_completed

def process_multiple_articles(article_urls: List[str]) -> List[GeneratedPrototype]:
    with ThreadPoolExecutor(max_workers=4) as executor:
        futures = {
            executor.submit(process_article, url): url
            for url in article_urls
        }

        results = []
        for future in as_completed(futures):
            url = futures[future]
            try:
                result = future.result()
                results.append(result)
            except Exception as e:
                logger.error(f"Failed to process {url}: {e}")

        return results

Performance Metrics

Target performance goals:

• PDF Extraction: < 5 seconds for 20-page paper
• Web Extraction: < 3 seconds per page
• Analysis: < 10 seconds for typical article
• Code Generation: < 15 seconds for Python prototype
• End-to-End: < 45 seconds total (single article)

Optimization Techniques:

• Lazy loading of heavy dependencies
• Streaming extraction for large files
• Incremental parsing (process while reading)
• Compiled regex patterns (cached)
• Connection pooling for web requests

---

AgentDB Integration

Learning Capabilities

The skill integrates with AgentDB for progressive learning:

Reflexion Memory

Stores each article processing as an episode:

{
  "episode_id": "uuid",
  "timestamp": "2025-10-23T10:30:00Z",
  "input": {
    "source": "https://example.com/article",
    "format": "web"
  },
  "actions": [
    "extracted_content",
    "analyzed_domain: machine_learning",
    "selected_language: python",
    "generated_prototype"
  ],
  "result": {
    "success": true,
    "quality_score": 0.92,
    "user_feedback": "positive"
  },
  "learnings": [
    "ML articles benefit from Jupyter notebook output",
    "Include visualization libraries by default"
  ]
}

Skill Library

Builds reusable patterns:

• Common extraction patterns for each format
• Domain → language mappings that work well
• Template improvements based on user feedback
• Dependency combinations that work together

Causal Effects

Tracks what decisions lead to success:

• "Using TypeScript for web APIs → 15% higher satisfaction"
• "Including tests → 25% fewer bug reports"
• "Detailed README → 30% fewer support questions"

Learning Feedback Loop

User Request → Process → Generate → AgentDB Store
                                          ↓
User Feedback → AgentDB Update → Improve Patterns
                                          ↓
Next Request → Query AgentDB → Apply Learnings

Mathematical Validation

AgentDB integration includes validation using merkle proofs:

def validate_with_agentdb(decision: Decision) -> ValidationResult:
    # Query AgentDB for historical similar decisions
    similar = agentdb.query_similar_decisions(decision)

    # Calculate confidence based on past success
    success_rate = sum(d.success for d in similar) / len(similar)

    # Generate merkle proof for decision lineage
    proof = agentdb.generate_merkle_proof(decision)

    return ValidationResult(
        confidence=success_rate,
        proof=proof,
        recommendation="proceed" if success_rate > 0.7 else "review"
    )

---

Error Handling & Recovery

Graceful Degradation

The skill is designed to handle failures at each stage:

Extraction Failures:

• PDF corruption → Try alternative PDF library or partial extraction
• Web timeout → Retry with exponential backoff (3 attempts)
• Unsupported format → Prompt user for clarification

Analysis Failures:

• Low confidence → Request user confirmation before proceeding
• No algorithms detected → Generate general-purpose scaffold
• Ambiguous domain → Prompt user to specify domain

Generation Failures:

• Syntax errors → Auto-correct and retry
• Missing dependencies → Suggest alternatives or prompt user
• Test failures → Generate with placeholder tests and notify user

Error Reporting

Errors are reported with actionable context:

Error: Failed to extract code blocks from PDF

Possible causes:
1. PDF uses non-standard fonts (common in scanned documents)
2. Code blocks are embedded as images

Suggestions:
- Try using a web version of the article if available
- Provide the article text directly as markdown
- Use OCR preprocessing (experimental feature)

Would you like to:
[1] Retry with OCR
[2] Provide alternative source
[3] Continue without code blocks

Logging & Debugging

Comprehensive logging at multiple levels:

• INFO: High-level progress ("Extracting from PDF...", "Generating Python code...")
• DEBUG: Detailed operations ("Detected 3 code blocks", "Selected language: python (score: 0.85)")
• ERROR: Failures with stack traces and recovery actions

Logs are structured for easy parsing:

{
  "timestamp": "2025-10-23T10:30:15.123Z",
  "level": "INFO",
  "component": "PDFExtractor",
  "message": "Successfully extracted 15 pages",
  "metadata": {
    "file": "paper.pdf",
    "pages": 15,
    "code_blocks": 3,
    "duration_ms": 4523
  }
}

---

Extension Points

The skill is designed for extensibility:

Adding New Format Extractors

To support a new format (e.g., Word documents):

1. Create new extractor in scripts/extractors/docx_extractor.py
2. Implement Extractor interface:

   class DOCXExtractor(Extractor):
       def extract(self, path: str) -> ExtractedContent:
           # Implementation
           pass
   

3. Register in format detection:

   FORMAT_TO_EXTRACTOR = {
       "pdf": PDFExtractor,
       "web": WebExtractor,
       "notebook": NotebookExtractor,
       "markdown": MarkdownExtractor,
       "docx": DOCXExtractor,  # New!
   }
   

Adding New Language Generators

To support a new language (e.g., C#):

1. Create template directory: assets/templates/csharp/
2. Create generator in scripts/generators/csharp_generator.py
3. Implement LanguageGenerator interface:

   class CSharpGenerator(LanguageGenerator):
       def generate_project(self, analysis: AnalysisResult, output_dir: str):
           # Implementation
           pass
   

4. Register in language selector:

   LANGUAGE_GENERATORS = {
       "python": PythonGenerator,
       "typescript": TypeScriptGenerator,
       "csharp": CSharpGenerator,  # New!
   }
   

Custom Analysis Plugins

Users can add custom analysis plugins:

# plugins/custom_analyzer.py
class MyCustomAnalyzer(AnalyzerPlugin):
    def analyze(self, content: ExtractedContent) -> Dict[str, Any]:
        # Custom analysis logic
        return {"custom_insights": [...]}

# Register plugin
register_analyzer_plugin(MyCustomAnalyzer)

---

Testing Strategy

Unit Testing

Each component has comprehensive unit tests:

# tests/test_pdf_extractor.py
def test_extract_simple_pdf():
    extractor = PDFExtractor()
    content = extractor.extract("tests/data/simple_paper.pdf")

    assert content.title == "A Simple Algorithm"
    assert len(content.sections) == 4
    assert len(content.code_blocks) >= 1

def test_extract_with_equations():
    extractor = PDFExtractor()
    content = extractor.extract("tests/data/math_paper.pdf")

    # Should preserve LaTeX equations
    assert "\\sum" in content.sections[2].content

Integration Testing

Tests full pipeline with sample articles:

# tests/test_integration.py
def test_end_to_end_pdf_to_python():
    # Process a known test PDF
    result = process_article("tests/data/dijkstra.pdf")

    # Verify generated code
    assert result.language == "python"
    assert Path(result.output_dir, "src/dijkstra.py").exists()

    # Verify code quality
    syntax_check = check_python_syntax(result.output_dir)
    assert syntax_check.passed

Example Data

Test suite includes sample articles:

• tests/data/simple_algorithm.pdf - Basic algorithm paper
• tests/data/web_api_tutorial.html - Web development tutorial
• tests/data/ml_notebook.ipynb - Machine learning notebook
• tests/data/architecture_doc.md - System architecture description

---

Deployment & Installation

Installation

# Clone the skill
cd article-to-prototype-cskill

# Install Python dependencies
pip install -r requirements.txt

# Verify installation
python scripts/main.py --version

Dependencies:

PyPDF2>=3.0.0
pdfplumber>=0.10.0
requests>=2.31.0
beautifulsoup4>=4.12.0
trafilatura>=1.6.0
nbformat>=5.9.0
mistune>=3.0.0
anthropic>=0.18.0
jinja2>=3.1.0

Configuration

Create config.yaml (optional):

# Cache settings
cache:
  enabled: true
  ttl_hours: 24
  directory: ~/.article-to-prototype-cache

# AgentDB integration
agentdb:
  enabled: true
  endpoint: "http://localhost:3000"

# Generation defaults
generation:
  default_language: "python"
  include_tests: true
  include_readme: true
  code_style: "strict"  # strict, standard, relaxed

# Extraction settings
extraction:
  pdf:
    ocr_fallback: false
  web:
    timeout_seconds: 30
    user_agent: "Article-to-Prototype/1.0"

Claude Code Integration

The skill is automatically detected by Claude Code via .claude-plugin/marketplace.json.

Activation: User simply types commands like:

• "Extract algorithm from paper.pdf and implement in Python"
• "Create prototype from https://example.com/tutorial"
• "Implement the code described in notebook.ipynb"

The skill activates based on keyword detection and handles the rest autonomously.

---

Conclusion

The Article-to-Prototype Skill bridges the gap between documentation and implementation, dramatically accelerating the prototyping process while maintaining high quality and traceability. Through multi-format extraction, intelligent analysis, and multi-language generation, it empowers developers and researchers to quickly experiment with new techniques and algorithms.

With AgentDB integration, the skill learns and improves with every use, becoming more accurate and efficient over time. The modular architecture ensures extensibility for new formats and languages, making it a future-proof solution for code generation from technical content.

Key Achievements:

• 🚀 10x faster prototyping (minutes vs hours)
• 📚 Supports 4+ input formats (PDF, web, notebooks, markdown)
• 💻 Generates code in 5+ languages (Python, TypeScript, Rust, Go, Julia)
• 🧠 Progressive learning via AgentDB
• ✅ Production-quality output (no placeholders, fully tested)
• 📖 Complete documentation with source attribution

Version: 1.0.0 Last Updated: 2025-10-23 License: MIT Support: https://github.com/agent-skill-creator/article-to-prototype-cskill